Personalized Treatment for Coronary Artery Disease Patients: A Machine Learning Approach

Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that considers personalized patient-specific characteristics. Using the electronic health records of 21,460 patients, we created data-driven models for personalized CAD management that significantly improve health outcomes relative to the standard of care. We develop binary classifiers to detect whether a patient will experience an adverse event due to CAD within a 10-year time frame. Combining the patients' medical history and clinical examination results, we achieve 81.5% AUC. For each treatment, we also create a series of regression models that are based on different supervised machine learning algorithms. We are able to estimate with average R squared = 0.801 the time from diagnosis to a potential adverse event (TAE) and gain accurate approximations of the counterfactual treatment effects. Leveraging combinations of these models, we present ML4CAD, a novel personalized prescriptive algorithm. Considering the recommendations of multiple predictive models at once, ML4CAD identifies for every patient the therapy with the best expected outcome using a voting mechanism. We evaluate its performance by measuring the prescription effectiveness and robustness under alternative ground truths. We show that our methodology improves the expected TAE upon the current baseline by 24.11%, increasing it from 4.56 to 5.66 years. The algorithm performs particularly well for the male (24.3% improvement) and Hispanic (58.41% improvement) subpopulations. Finally, we create an interactive interface, providing physicians with an intuitive, accurate, readily implementable, and effective tool

Pre-diagnostic prescribing patterns in dyspnoea patients with as-yet-undiagnosed lung cancer: A longitudinal study of linked primary care and cancer registry data

Introduction: Patients with as-yet undiagnosed lung cancer (LC) can present to primary care with non-specific symptoms such as dyspnoea, often in the context of pre-existing chronic obstructive pulmonary disease (COPD). Related medication prescriptions pre-diagnosis might represent opportunities for earlier diagnosis, but UK evidence is limited. Consequently, we explored prescribing patterns of relevant medications in patients who presented with dyspnoea in primary care and were subsequently diagnosed with LC. // Method: Linked primary care (Clinical Practice Research Datalink) and National Cancer Registry data were used to identify 5434 patients with incident LC within a year of a dyspnoea presentation in primary care between 2006 and 2016. Primary care prescriptions relevant to dyspnoea management were examined: antibiotics, inhaled medications, oral steroids, and opioid analgesics. Poisson regression models estimated monthly prescribing rates during the year pre-diagnosis. Variation by COPD status (52 % pre-existing, 36 % COPD-free, 12 % new-onset) was examined. Inflection points were identified indicating when prescribing rates changed from the background rate. // Results: 63 % of patients received 1 or more relevant prescriptions 1–12 months pre-diagnosis. Pre-existing COPD patients were most prescribed inhaled medications. COPD-free and new-onset COPD patients were most prescribed antibiotics. Most patients received 2 or more relevant prescriptions. Monthly prescribing rates of all medications increased towards time of diagnosis in all patient groups and were highest in pre-existing COPD patients. Increases in prescribing activity were observed earliest in pre-existing COPD patients 5 months pre-diagnosis for inhaled medications, antibiotics, and steroids. // Conclusion: Results indicate that a diagnostic window of appreciable length exists for potential earlier LC diagnosis in some patients. Lung cancer diagnosis may be delayed if early symptoms are misattributed to COPD or other benign conditions

Machine learning applications on neonatal sepsis treatment: a scoping review.

INTRODUCTION: Neonatal sepsis is a major cause of health loss and mortality worldwide. Without proper treatment, neonatal sepsis can quickly develop into multisystem organ failure. However, the signs of neonatal sepsis are non-specific, and treatment is labour-intensive and expensive. Moreover, antimicrobial resistance is a significant threat globally, and it has been reported that over 70% of neonatal bloodstream infections are resistant to first-line antibiotic treatment. Machine learning is a potential tool to aid clinicians in diagnosing infections and in determining the most appropriate empiric antibiotic treatment, as has been demonstrated for adult populations. This review aimed to present the application of machine learning on neonatal sepsis treatment. METHODS: PubMed, Embase, and Scopus were searched for studies published in English focusing on neonatal sepsis, antibiotics, and machine learning. RESULTS: There were 18 studies included in this scoping review. Three studies focused on using machine learning in antibiotic treatment for bloodstream infections, one focused on predicting in-hospital mortality associated with neonatal sepsis, and the remaining studies focused on developing machine learning prediction models to diagnose possible sepsis cases. Gestational age, C-reactive protein levels, and white blood cell count were important predictors to diagnose neonatal sepsis. Age, weight, and days from hospital admission to blood sample taken were important to predict antibiotic-resistant infections. The best-performing machine learning models were random forest and neural networks. CONCLUSION: Despite the threat antimicrobial resistance poses, there was a lack of studies focusing on the use of machine learning for aiding empirical antibiotic treatment for neonatal sepsis

Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II

Problem Definition. Increasing costs of healthcare highlight the importance of effective disease prevention. However, decision models for allocating preventive care are lacking. Methodology/Results. In this paper, we develop a data-driven decision model for determining a cost-effective allocation of preventive treatments to patients at risk. Specifically, we combine counterfactual inference, machine learning, and optimization techniques to build a scalable decision model that can exploit high-dimensional medical data, such as the data found in modern electronic health records. Our decision model is evaluated based on electronic health records from 89,191 prediabetic patients. We compare the allocation of preventive treatments (metformin) prescribed by our data-driven decision model with that of current practice. We find that if our approach is applied to the U.S. population, it can yield annual savings of $1.1 billion. Finally, we analyze the cost-effectiveness under varying budget levels. Managerial Implications. Our work supports decision-making in health management, with the goal of achieving effective disease prevention at lower costs. Importantly, our decision model is generic and can thus be used for effective allocation of preventive care for other preventable diseases.Comment: Accepted by Manufacturing & Service Operations Managemen

Bugs, Drugs and Data: Antibiotic Resistance, Prevalence and Prediction of Bug-Drug Mismatch using Electronic Health Records (EHR) Data

Title from PDF of title page viewed December 14, 2021Dissertation advisor: An-Lin ChengVitaIncludes bibliographical references (pages 92-130)Thesis (Ph.D.)--School of Medicine, School of Computing and Engineering, and School of Biological and Chemical Sciences. University of Missouri--Kansas City, 2021Bug-Drug Mismatch (BDM) occurrences are an important and modifiable category of inappropriate antibiotic therapy (IAAT) that increases adverse outcomes for patients and drives overall antibiotic resistance (AR). Surveillance of baseline AR, emerging trends in resistance among priority bacterial pathogens and prevalence of BDM with respect to the age of the patients and the type of health care-setting are required due to differences in antimicrobial need and use in these populations. Additionally, very little is known about the risk factors associated with BDM occurrence. We performed a retrospective study using de-identified, electronic health record (EHR) data in the Cerner Health Facts™ data warehouse. We assessed antibiotic susceptibility data between the years 2012 to 2017 and visualized the slope coefficient from linear regression to compare changes in resistance over time. We examined the prevalence of BDM for critically important antibiotics and clinically relevant pathogens between the year 2009 to 2017 in four groups of patients: adults; children; children treated in freestanding pediatric facilities and children treated in blended facilities (adults and children). We implemented multiple logistic regression as a reference model to identify risk factors for BDM occurrences and compared the predictive performance measure with 4 machine learning models (logistic regression with lasso regularization, random forest, gradient boosted decision tree and deep neural network). The trends in resistance rates to clinically relevant antibiotics were influenced by age and care setting. BDM prevalence for several critically important antibiotics differed between children and adults as well as within pediatric and blended facilities. Risk factors such as age of the patient, patient comorbidities and size of the facility were significantly associated with BDM occurrence. Additionally, the machine learning models developed in our study has a high predictive ability (C-statistic), higher sensitivity, specificity, positive predictive value and positive likelihood ratio to identify BDM occurrence than the reference model. This study describes the utility of data visualization to interpret large scale EHR data on the trends of AR, prevalence and risk factors of BDM which are critical in tailoring antibiotic stewardship efforts to improving appropriate antibiotic prescribing and ultimately reduce AR.Introduction -- Background -- Variation in antibiotic resistance patterns for children and adults treated at 166 non-affiliated facilities -- Differences in the prevalence of definitive bug-drug mismatch (BDM) therapy between adults and children by care setting -- Predicting bug-drug mismatch (BDM) occurrence in EHR data using machine Learning models -- Conclusio

Time-Series Embedded Feature Selection Using Deep Learning: Data Mining Electronic Health Records for Novel Biomarkers

As health information technologies continue to advance, routine collection and digitisation of patient health records in the form of electronic health records present as an ideal opportunity for data-mining and exploratory analysis of biomarkers and risk factors indicative of a potentially diverse domain of patient outcomes. Patient records have continually become more widely available through various initiatives enabling open access whilst maintaining critical patient privacy. In spite of such progress, health records remain not widely adopted within the current clinical statistical analysis domain due to challenging issues derived from such “big data”.Deep learning based temporal modelling approaches present an ideal solution to health record challenges through automated self-optimisation of representation learning, able to man-ageably compose the high-dimensional domain of patient records into data representations able to model complex data associations. Such representations can serve to condense and reduce dimensionality to emphasise feature sparsity and importance through novel embedded feature selection approaches. Accordingly, application towards patient records enable complex mod-elling and analysis of the full domain of clinical features to select biomarkers of predictive relevance.Firstly, we propose a novel entropy regularised neural network ensemble able to highlight risk factors associated with hospitalisation risk of individuals with dementia. The application of which, was able to reduce a large domain of unique medical events to a small set of relevant risk factors able to maintain hospitalisation discrimination.Following on, we continue our work on ensemble architecture approaches with a novel cas-cading LSTM ensembles to predict severe sepsis onset within critical patients in an ICU critical care centre. We demonstrate state-of-the-art performance capabilities able to outperform that of current related literature.Finally, we propose a novel embedded feature selection application dubbed 1D convolu-tion feature selection using sparsity regularisation. Said methodology was evaluated on both domains of dementia and sepsis prediction objectives to highlight model capability and generalisability. We further report a selection of potential biomarkers for the aforementioned case study objectives highlighting clinical relevance and potential novelty value for future clinical analysis.Accordingly, we demonstrate the effective capability of embedded feature selection ap-proaches through the application of temporal based deep learning architectures in the discovery of effective biomarkers across a variety of challenging clinical applications

Machine learning in oral squamous cell carcinoma: current status, clinical concerns and prospects for future-A systematic review

Background: Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care. Objectives: This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice. Data sources: We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC. Eligibility criteria: Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered. Data extraction: Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies. Results: A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations. Conclusion: Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary.Peer reviewe

Developing clinical prediction models for diabetes classification and progression

Patients with type 1 and type 2 diabetes have very different treatment and care requirements. Overlapping phenotypes and lack of clear classification guidelines make it difficult for clinicians to differentiate between type 1 and type 2 diabetes at diagnosis. The rate of glycaemic deterioration is highly variable in patients with type 2 diabetes but there is no single test to accurately identify which patients will progress rapidly to requiring insulin therapy. Incorrect treatment and care decisions in diabetes can have life-threatening consequences. The aim of this thesis is to develop clinical prediction models that can be incorporated into routine clinical practice to assist clinicians with the classification and care of patient diagnosed with diabetes. We addressed the problem first by integrating features previously associated with classification of type 1 and type 2 diabetes to develop a diagnostic model using logistic regression to identify, at diagnosis, patients with type 1 diabetes. The high performance achieved by this model was comparable to that of machine learning algorithms. In patients diagnosed with type 2 diabetes, we found that patients who were GADA positive and had genetic susceptibility to type 1 diabetes progressed more rapidly to requiring insulin therapy. We built upon this finding to develop a prognostic model integrating predictive features of glycaemic deterioration to predict early insulin requirement in adults diagnosed with type 2 diabetes. The three main findings of this thesis have the potential to change the way that patients with diabetes are managed in clinical practice. Use of the diagnostic model developed to identify patients with type 1 diabetes has the potential to reduce misclassification. Classifying patients according to the model has the benefit of being more akin to the treatment needs of the patient rather than the aetiopathological definitions used in current clinical guidelines. The design of the model lends itself to implementing a triage-based approach to diabetes subtype diagnosis. Our second main finding alters the clinical implications of a positive GADA test in patients diagnosed with type 2 diabetes. For identifying patients likely to progress rapidly to insulin, genetic testing is only beneficial in patients who test positive for GADA. In clinical practice, a two-step screening process could be implemented - only patients who test positive for GADA in the first step would go on for genetic testing. The prognostic model can be used in clinical practice to predict a patient’s rate of glycaemic deterioration leading to a requirement for insulin. The availability of this data will enable clinical practices to more effectively manage their patient lists, prioritising more intensive follow up for those patients who are at high risk of rapid progression. Patients are likely to benefit from tailored treatment. Another key clinical use of the prognostic model is the identification of patients who would benefit most from GADA testing saving both inconvenience to the patient and a cost-benefit to the health service

Antimicrobial Resistance and Machine Learning: Challenges and Opportunities

Antimicrobial Resistance (AMR) has been identified by the World Health Organisation (WHO) as one of the top ten global health threats. Inappropriate use of antibiotics around the world and in particular in Low-to-Middle-Income Countries (LMICs), where antibiotics use and prescription are poorly managed, is considered one of the main reasons for this problem. It is projected that the COVID-19 pandemic will accelerate the threat of AMR due to the increasing use of antibiotics across the world, and especially in countries with limited resources. In recent years, machine learning-based methods showed promising results and proved capable of providing the necessary tools to inform antimicrobial prescription and combat AMR. This timely paper provides a critical and technical review of existing machine learning-based methods for addressing AMR. First, an overview of the AMR problem as a global threat to public health, and its impact on countries with limited resources (LMICs) are presented. Then, a technical review and evaluation of existing literature that utilises machine learning to tackle AMR are provided with emphasis on methods that use readily available demographic and clinical data as well as microbial culture and sensitivity laboratory data of clinical isolates associated with multi-drug resistant infections. This is followed by a discussion of challenges and limitations that are considered barriers to scaling up the use of machine learning to address AMR. Finally, a framework for accelerating the use of AMR data-driven framework, and building a feasible solution that can be realistically implemented in LMICs is presented with a discussion of future directions and recommendations